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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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learning. A record of contributing to building and maintaining effective and productive links locally and nationally with the discipline, profession and wider community. Tasmanian Working with Vulnerable
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experience in machine learning and artificial intelligence who can teach in areas relevant to AI including machine learning, deep learning, natural language processing, reinforcement learning, robotics and
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(PyTorch, scientific Python) with solid experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image
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) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane intelligence (e.g., reinforcement learning for scheduling
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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the dynamics of potentially illegal waste deposits. The research will apply deep learning and computer vision techniques to identify regions within an image where there is an increase or decrease in
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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) enables computations to be performed directly on encrypted data without knowledge of the deciphering key, offering significant potential for privacy-preserving deep learning. However, conventional neural
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and experienced AI technical leader responsible for driving the technical strategy, design, and implementation of Artificial Intelligence and Machine Learning (AI/ML) solutions across the university